Multi-view Clustering: A Survey
In the big data era, the data are generated from different sources or observed from different views. These data are referred to as multi-view data. Unleashing the power of knowledge in multi-view data is very important in big data mining and analysis. This calls for advanced techniques that consider...
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| Format: | Article |
| Language: | English |
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Tsinghua University Press
2018-06-01
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| Series: | Big Data Mining and Analytics |
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| Online Access: | https://www.sciopen.com/article/10.26599/BDMA.2018.9020003 |
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| author | Yan Yang Hao Wang |
| author_facet | Yan Yang Hao Wang |
| author_sort | Yan Yang |
| collection | DOAJ |
| description | In the big data era, the data are generated from different sources or observed from different views. These data are referred to as multi-view data. Unleashing the power of knowledge in multi-view data is very important in big data mining and analysis. This calls for advanced techniques that consider the diversity of different views, while fusing these data. Multi-view Clustering (MvC) has attracted increasing attention in recent years by aiming to exploit complementary and consensus information across multiple views. This paper summarizes a large number of multi-view clustering algorithms, provides a taxonomy according to the mechanisms and principles involved, and classifies these algorithms into five categories, namely, co-training style algorithms, multi-kernel learning, multi-view graph clustering, multi-view subspace clustering, and multi-task multi-view clustering. Therein, multi-view graph clustering is further categorized as graph-based, network-based, and spectral-based methods. Multi-view subspace clustering is further divided into subspace learning-based, and non-negative matrix factorization-based methods. This paper does not only introduce the mechanisms for each category of methods, but also gives a few examples for how these techniques are used. In addition, it lists some publically available multi-view datasets. Overall, this paper serves as an introductory text and survey for multi-view clustering. |
| format | Article |
| id | doaj-art-a9c0b5cd97034d02b20c411f43147384 |
| institution | Kabale University |
| issn | 2096-0654 |
| language | English |
| publishDate | 2018-06-01 |
| publisher | Tsinghua University Press |
| record_format | Article |
| series | Big Data Mining and Analytics |
| spelling | doaj-art-a9c0b5cd97034d02b20c411f431473842025-02-02T06:49:25ZengTsinghua University PressBig Data Mining and Analytics2096-06542018-06-01128310710.26599/BDMA.2018.9020003Multi-view Clustering: A SurveyYan Yang0Hao Wang1<institution content-type="dept">School of Information Science and Technology</institution>, <institution>Southwest Jiaotong University</institution>, <city>Chengdu</city> <postal-code>611756</postal-code>, <country>China</country>.<institution content-type="dept">School of Information Science and Technology</institution>, <institution>Southwest Jiaotong University</institution>, <city>Chengdu</city> <postal-code>611756</postal-code>, <country>China</country>.In the big data era, the data are generated from different sources or observed from different views. These data are referred to as multi-view data. Unleashing the power of knowledge in multi-view data is very important in big data mining and analysis. This calls for advanced techniques that consider the diversity of different views, while fusing these data. Multi-view Clustering (MvC) has attracted increasing attention in recent years by aiming to exploit complementary and consensus information across multiple views. This paper summarizes a large number of multi-view clustering algorithms, provides a taxonomy according to the mechanisms and principles involved, and classifies these algorithms into five categories, namely, co-training style algorithms, multi-kernel learning, multi-view graph clustering, multi-view subspace clustering, and multi-task multi-view clustering. Therein, multi-view graph clustering is further categorized as graph-based, network-based, and spectral-based methods. Multi-view subspace clustering is further divided into subspace learning-based, and non-negative matrix factorization-based methods. This paper does not only introduce the mechanisms for each category of methods, but also gives a few examples for how these techniques are used. In addition, it lists some publically available multi-view datasets. Overall, this paper serves as an introductory text and survey for multi-view clustering.https://www.sciopen.com/article/10.26599/BDMA.2018.9020003data miningconditional functional dependencybig datadata quality |
| spellingShingle | Yan Yang Hao Wang Multi-view Clustering: A Survey Big Data Mining and Analytics data mining conditional functional dependency big data data quality |
| title | Multi-view Clustering: A Survey |
| title_full | Multi-view Clustering: A Survey |
| title_fullStr | Multi-view Clustering: A Survey |
| title_full_unstemmed | Multi-view Clustering: A Survey |
| title_short | Multi-view Clustering: A Survey |
| title_sort | multi view clustering a survey |
| topic | data mining conditional functional dependency big data data quality |
| url | https://www.sciopen.com/article/10.26599/BDMA.2018.9020003 |
| work_keys_str_mv | AT yanyang multiviewclusteringasurvey AT haowang multiviewclusteringasurvey |